flip, flops and foreclosures: anatomy of a real estate bubble
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Electronic copy available at: http://ssrn.com/abstract=1658008
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Flips, Flops and Foreclosures: Anatomy of a Real Estate Bubble
Craig A. Depken II UNC-Charlotte
Harris Hollans Auburn University
Steve Swidler* Auburn University
JEL Classification: G11, G21, R31 Key Words: Flipping, Mortgages, Foreclosure, Speculation, Real Estate
* Corresponding Author: Steve Swidler
Department of Finance 303 Lowder Business Bldg. Auburn University, AL 36849 [email protected] (334)844-3014 (334)844-4960 fax
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Electronic copy available at: http://ssrn.com/abstract=1658008
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Flips, Flops and Foreclosures: Anatomy of a Real Estate Bubble
Abstract
This paper examines the anatomy of a real estate bubble. In the process, we identify three
phases of the markets evolution: in the first phase, a large percentage of transactions are
speculative or flips causing prices to rapidly increase; in phase two, flipping loses its
profitability; and in phase three, there are an increasing number of foreclosures leading to falling
prices. An illustration of this anatomy is provided by the evolution of the Las Vegas
metropolitan housing market from 1994 through 2009. The descriptive analysis of the Las Vegas
market is augmented with causality tests which show that the percentage change in price was the
driving force behind all three phases in the markets evolution.
JEL Classifications: G11, G21, R31 Key Words: Flipping, Mortgages, Foreclosure, Speculation, Real Estate
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1. Introduction
Conventional wisdom in the United States blames the housing market as the first
domino that fell in the lead-up to the recession that began in early 2007 (see, e.g., Shiller,
2009). Some have alluded to a housing bubble that was unsustainable and which caused
individuals to have a false perception that housing prices would continue to increase, thereby
making it profitable to purchase more expensive homes or to speculate on residential real estate
in so-called flipping (see Wheaton and Nechayev, 2008). Indeed, the popularity of flipping
might be reflected in popular culture in which television shows such as Flip this house (A&E
network) and Flip that House (TLC) were amongst the most popular television shows in the
early 2000s. As markets have cooled off or even collapsed, these shows have since been replaced
by less bullish shows such as late night television infomercials selling foreclosed residential
condominiums.
This paper examines the anatomy of a real estate bubble. In the process, we identify three
phases of the markets evolution: in the first phase, a large percentage of transactions are
speculative or flips causing prices to rapidly increase; in phase two, flipping loses its
profitability and many individuals are caught holding the bag, (i.e., cannot resell their house at
a higher high price); and in phase three, there are an increasing number of foreclosures leading to
falling prices. Eventually properties held by banks (shadow inventory) must be sold or destroyed
before the market can recover and stabilize.
To illustrate a real estate bubble, we investigate the evolution of the Las Vegas
metropolitan housing market from 1994 through 2009. We begin with positive economic
analysis that is mainly descriptive in nature and graphically captures the three phases of the Las
Vegas real estate bubble. Our subsequent analysis formally investigates the extent to which flips,
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foreclosures and percentage change in price are related to each other. Granger Causality tests
imply that percentage change in price is the driving force behind flipping and foreclosure
activity, but that flips and foreclosures are not directly related to each other. Finally, normative
analysis of a real estate bubble suggests a number of policy propositions that might be
considered by lawmakers and real estate professionals.
While local housing markets follow idiosyncratic cycles, price trends include a
systematic component related to certain factors. For instance, the literature has established that
housing prices tend to increase as the local population increases, as new housing stock replaces
older homes, as local incomes increase, and as the supply of developable land decreases (see,
e.g., Brueckner, 1980 and Capozza and Helsley, 1989). On the other hand, (moderate)
recessions are not necessarily associated with a fall in housing prices but rather with a reduction
in the number of houses sold in any particular time period and an increase in the time-on-market
for existing houses. In other words, house prices tend to be sticky downward. Nevertheless, a
sufficiently severe recession might induce price decreases. For instance, Case and Quigley
(2008) find that, in the last half of 2006, sales activity slowed, but housing prices in Boston
declined only moderately at the beginning of the downturn. However, as the recession continued
to deepen, Boston housing prices at the end of 2009 were approximately 17% below their peak,
falling to their 2003 levels (as measured by the Case-Shiller index).
Although stable and increasing housing prices are frequently thought of as the norm, it is
possible for local housing markets to experience dramatic increases (and decreases) in price.
Whether such price volatility is generated by artificially restrained supply, for instance through
overly restrictive zoning or land-use policies, or through artificially enhanced demand, rapidly
escalating prices might induce individuals to speculate on residential properties in the form of
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flipping. Flipping entails purchasing a residential property, perhaps improving the property
through cosmetic or structural changes, and attempting to rapidly resell the property for a profit.
House flipping contributes to an increase in the demand for existing properties, thereby pushing
up price. However, house flipping might also be a rational response to other market signals such
as a rapidly increasing population or relatively easy credit for potential home-buyers (Wheaton
and Nechayev, 2008). Estimating the relative influence of these possible factors is mainly an
empirical exercise.
Factors that lead to a dramatic escalation in housing prices cannot be expected to last
forever. Thus, a tapering-off period follows during which price increases moderate, the profits
from flipping fall, and there is a decline in the proportion of sales that are flips. This reduced
exuberance might presage an actual, and potentially dramatic, decline in price. If this is the case,
those who attempted to participate in flipping toward the end of the exuberant period and
many who purchased at the peak of the market will find themselves holding a depreciating asset.
In this environment, the flipping period is followed by a period of flops and finally a
potential for foreclosures as some individuals find it in their best interest to default on their
mortgage rather than trying to sell the property for a loss. The subsequent analysis chronicles a
cycle of flips, flops and foreclosures in Clark County, Nevada, a district that essentially
comprises the Las Vegas metropolitan area.
2. Data and Definitions of Transaction Type
The data sample used in this study describes 541,373 separate residential property
transactions from Clark County, Nevada from 1994:q1 through 2009:q4, obtained from the Clark
County tax assessors office. The data describe, among other things, the transaction price, the
transaction type, the date of the transaction, and a unique parcel identifier.
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We are able to examine up to nine separate transactions for each parcel, although the vast
majority of properties have less than four transactions during the sample period. To facilitate the
use of such a large data set and to provide a level of aggregation that might inform policy
discussion, the analysis translates each transaction date into the appropriate quarter and year. The
subsequent analysis is then undertaken on a quarterly basis.
Each transfer of property is coded by the Clark County Tax Assessors office according
to the transaction (sale) type. There are three categories that we examine: i) Recorded Value
denoting an arms-length transaction (coded with an R), ii) Trustees Deed is the amount bid
at foreclosure auction on the trustees deed (coded with a T), and iii) Foreclosure is a transfer
indicating a resale after foreclosure (coded with an F). These three sale types constitute the
bulk of all transactions filed at the tax assessors office and are the most important categories for
the purposes of our investigation.
Table 1 lists the distribution of residential transactions by sales type. For the entire
sample period, there are 464,093 R transactions, 47,320 T transactions, and 29,960 F
transactions recorded. Examining tax records on a quarterly basis, total transactions trend
upwards and reach a peak of more than fifteen thousand sales in the third quarter of 2005. On a
percentage basis, arms-length transactions (R) constitute more than 95% of all sales through the
fourth quarter of 2006. House prices in Las Vegas (as measured by the Case-Shiller index)
reflected the vigorous sales activity of this period, and after large run-ups in 2004 and 2005,
prices eventually crested in the second quarter of 2006.
To put the figures in perspective, Figure 1 depicts the number of R, T and F transactions
for the period 2004:q1 through 2009:q4. As can be seen, arms-length transactions dominate the
distribution up until the end of 2006. In 2007, the F and T transactions begin to increase in
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number and eventually foreclosure activity constitutes the leading share of sales. The properties
in foreclosure combined with additional new and used properties on the market might be
expected to exert downward pressure on price and alter the expectations of potential buyers
about future price changes. In fact, the last three years of the sample period witnessed rapid
price declines in the Las Vegas market.
The dispersion of foreclosure activity is not evenly distributed across the 104 different
tax districts of Clark County. Table 2 depicts the R, T and F transactions for the twenty one tax
districts that constitute our sample and include all areas with more than 1,000 residential
transactions during the period of analysis. Of particular note is that the districts with the largest
foreclosure activity tend to have the lowest per capita income in the area. In particular,
foreclosures were more than 17% of total sales in the low income districts of North Las Vegas,
Sunrise Manor and Whitney.
It is important that the county assessor accurately characterize each transaction as the
data serves as a foundation for mass-appraisal models used to determine fair market value for
ad valorem tax purposes. As such, arms-length transactions denoted as R transactions in the data
serve as the benchmark for market value estimation. Frequently, arms-length transactions are
thought of as a sale between a willing seller and a willing buyer.
A trustees deed transaction (coded T) denotes a foreclosure sale and signifies that the
property either resides in the Real Estate Owned (REO) inventory of the lender or was purchased
at the foreclosure sale by an owner/investor. Typically the lender is the winning bidder at
auction and the recorded sale price of the T transaction represents the amount bid on the trustees
deed. A T transaction may also represent a deed-in-lieu of foreclosure, which entails the lender
repossessing the house without pursuing a foreclosure on the property, with the result that the
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homeowner loses whatever equity they have in the house. Presumably there is little or, more
likely, negative equity precipitating the transference of the property. In a deed-in-lieu of
foreclosure, the lender often agrees to not pursue the individual home owner for recourse, which
has arguably become easier under the recently passed rules of the Trouble Asset Relief Program
(TARP) and the American Recovery and Reinvestment Act (ARRA).
Early in the sample period, an F code denoted a deed-in-lieu of foreclosure transaction.
With the recent increase in foreclosure activity, the county changed an F transaction to mean that
the transfer of a property is a resale after foreclosure. The typical example of a recently coded F
transaction would be the sale of the house by the lender (who acquired the trustees deed through
a T transaction) to a new homeowner. If the sale price is thought to be different from market
value, the county then codes this as an F transaction.
Table 3 gives the flavor of foreclosure activity in Clark County and how the coding of
deed-in-lieu of foreclosure changed over the sample period. In the early part of the sample
(1994:q1-2006:q4), homes going into foreclosure were predominantly coded T transactions that
signaled transference of a trustees deed. In a typical quarter, nearly 161 homes were T
transactions, while 5 were coded F by the county and primarily denoted a deed-in-lieu of
foreclosure sale. Looking at the last three columns of Table 3, whether designated a T or an F
for homes going into foreclosure, virtually all subsequent sales were coded R by the county.
This implies that almost all houses were sold to the new homeowners at market value, i.e., the
county considered the transaction between the lender and new homeowner as an arms-length
sale.
Over the more recent sample period (2007:q1-2009:q4), Table 3 illustrates two important
changes that occurred. First, there were virtually no examples of a T (trustees deed) followed
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by an F (deed-in-lieu of foreclosure) in the early sample period. However, T then F becomes the
predominant foreclosure sequence of transactions moving into the latter sample period and
implies that for most of 2007-2009, T transactions included both trustees deed and deed-in-lieu
of foreclosure transactions. Moreover, an F transaction in 2007-2009 refers to a resale after
foreclosure. In the third column, the few hundred examples of F followed by R found in 2007-
2009 presumably denote properties that sold in an earlier period as a deed-in-lieu of foreclosure,
but then were coded as an arms-length transaction on the subsequent sale.
Of potentially more interest is the second change made in recent years. Whereas it has
already been noted that from 1994-2006 virtually all second sales in the foreclosure sequence
were considered R transactions, by 2009 more than 90% of second sales were coded F by the
county. This has important implications for housing valuation. In recent years, the county no
longer considered the vast majority of the foreclosure resales as an arms-length transaction, and
therefore, they would not likely be used in any tax valuation model or price index.
3. Property Flipping
As documented earlier, the rapid rise in housing prices from 2003-2005 corresponded to a
period that experienced a high number of sales. The county designated virtually all transactions
as arms-length (R). Many of these sales involved property flipping.
Depken, Hollans, and Swidler (2009), define a flip as the purchase of a home with the
intent of quickly reselling the property at a higher price. They examine all cases that involve two
arms-length transactions for a property within a two year window. Two years is a relevant time
frame as the Internal Revenue Service allows any capital gains to be excluded from taxable
income if the seller has used the home as his or her primary residence for two of the previous
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five years. This definition does not depend upon flipping motivation or economic profitability,
but rather focuses on the short-term investment horizon that is the epitome of house flipping.
House flipping is often depicted as purchasing property in poor condition at a discount,
renovating the house and then selling it at or near full market value. This is sometimes referred to
as a fix and flip and has been the basis for a number of reality television shows. However, this
is not the only situation in which a property might be ripe for flipping. Some properties might
be purchased at a discount due to forced circumstances such as relocation, divorce, or a pending
foreclosure. Such situations might provide for a nominally profitable opportunity if the house can
be resold at market value in a relatively short amount of time.
Given multiple transactions for a given property, it is possible to identify, ex post, which
transactions are the front-end of an eventual house flip, called the buy-side flip transaction, and
which are the back-end of an eventual house flip, called the sell-side flip transaction. This, in
turn, allows for the calculation of any economic profits due to flipping. House flipping, similar
to any speculative activity, is an inherently risky proposition, and higher risk suggests higher
expected return. Depken, Hollans, and Swidler (2009) find that flippers earned positive
economic profits in the Las Vegas residential housing market up through 2005, in large part,
reflecting sell-side flip prices that tended to be higher than other similar arms-length transactions.
The premium on the sell-side transaction might indicate improvement to the property not
captured in the tax records. However, the housing stock in Las Vegas is relatively young, and
many flips were of new or nearly new houses. It is unlikely that sell-side premiums were
primarily a reflection of home improvements.
Alternatively, a sales premium might obtain due to the circumstances of flipping. If
information is costly to obtain, flippers may have superior knowledge of the local real estate
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market. Since there is no pressure to relocate, flippers can take time to search for buyers offering
the highest price for the property. Still another possibility due to asymmetric information is that
sell-side flips might carry a premium because of illegal activity. For instance, appraisers might
collude with mortgage originators and the flippers broker to provide an inflated appraisal of the
flipped property. Inflated prices might then be the result of mortgage companies artificially
stimulating demand by qualifying buyers for more expensive homes. To limit predatory
flipping, the Department of Housing and Urban Development promulgated a set of guidelines in
2004 (amended in 2006) that prohibited Federal Housing Authority (FHA)-insured mortgage
financing for properties re-sold within 90 days. These guidelines, however, did little to stop
mortgage fraud that has been the focus of, Operation Stolen Dreams, an FBI campaign that
targets illegal flipping activity, loan origination schemes, and equity skimming
(http://www.fbi.gov/page2/june10/mortgage_061710.html).
To get a better idea of Las Vegas flipping activity during the sample period, Figure 2
depicts quarterly sell-side and buy-side flip transactions as a percentage of all arms-length sales.
As can be seen, buy-side and sell-side transactions comprise a small percentage of all
transactions until sometime after 2002 when the buy-side flip transactions break the ten percent
barrier. Eventually, the buy-side flip transactions peaks at approximately 23% of all arms-length
transactions in the first quarter of 2004 after which the percentage drops dramatically. The
falling number of buy-side flip transactions coincides with the declining profitably of flipping
and the eventual fall in housing prices (Depken, Hollans, and Swidler, 2009).
Figure 2 also exhibits a pattern of seasonality in the flip transactions data. Over the entire
sample period, there is no statistically meaningful difference between the percentage of buy-side
transactions in quarters 1, 2 or 3. However, there is a statistically significant decline in the
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percentage of buy-side transactions in the fourth quarter (by approximately 3.8 percent between
q1 and q4, p-value=.032). There exists a different but distinct pattern in sell-side flip transactions
as well: there is a positive and slightly significant increase in sell-side flip transactions in quarter
4 (by approximately 2.7 percent between q1 and q4, p-value=.098) although there is no
statistically meaningful difference between quarter 1 and quarter 2 or quarter 3.
When focus is restricted to the period starting with 2004:q1, the seasonal patterns
disappear for both buy and sell-side flip transactions. As can be seen in Figure 3, the percentage
of buy-side flip transactions falls below the percentage of sell-side flip transactions by the third
quarter of 2004, that is, buy-side flip transactions fall before the Las Vegas market experienced
dramatic decreases in prices and increases in foreclosure activity. Moreover, Figure 3 indicates
that flipping activity never actually reached zero; even during the dramatic downward adjustment
of the market after 2006 there were still some properties that speculators felt were potentially
profitable flips.
4. Descriptive Analysis of the Las Vegas Housing Bubble
Figure 4 depicts an evolution of flips, foreclosures, housing prices and price changes in
Clark County over the entire sample period. The bar graph above the x-axis represents the
percentage of all transactions identified ex post as one side of a flip (both buy-side and sell-side
each quarter). The bar graph below the horizontal axis illustrates the number of trustee deeds (T)
and foreclosures (F) as a percentage of total transactions. The percentage of flips and
foreclosures are then drawn against the median price of all arms-length transactions (solid line)
and the annualized percentage change in median price for each quarter (dashed line). In
chronicling the boom to bust housing cycle in the Las Vegas area, the graph can be neatly
divided into three distinct regimes.
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Flips 1994:q1 through 2005:q4
Between 1994 and 2000, the great majority of sales were arms-length transactions, with
relatively few flips or foreclosures. During this period, quarterly price changes were small
(approximating the nominal inflation rate), and the median transaction price in the Las Vegas
market remained relatively modest. After 2000, the percentage change in prices increased and
median prices followed accordingly. Seemingly in response to these price changes, flipping
activity also increased.
By 2004, flipping activity rose to roughly 40% of all housing transactions in Las Vegas,
and while perhaps not clear ex ante, flipping activity of this magnitude could not be sustained
forever. The artificial stimulus in demand from flipping, in turn, helped fuel an increase in new
homes built. The housing stock nearly doubled in Las Vegas from 2000 through 2008; however,
Clark Countys population grew only 33.9% (from 1.39 million to 1.87 million people) during
the same period. Flippers, home builders and (non-flip) sellers, eventually found it more
difficult to find buyers leading to the next stage of the bubble.
Flops 2006:q1 through 2007:q4
Figure 5 replicates the previous graph, but centers on the transition period between flips
and foreclosures. As flippers found it more difficult to locate buyers for their properties, price
increases attenuated and prices eventually peaked in 2006. In many cases, flips were no longer
profitable leading to economic losses and a fall in flipping activity. A reduction in flips occurred
because fewer investors decided to buy properties for flipping, and those that did could not
always sell them within two years. For many investors, 2006-2007 was a period of flops, and by
2008, Figure 5 shows that flipping activity had fallen to less than 5% of all transactions. This
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contributed to further price declines, and starting in 2008, foreclosures constituted the majority
of transactions in the Las Vegas area.
Foreclosures 2008:q1 through 2009:q4
In the final phase, flipping was not economically profitable as median prices continued to
decline, potential buyers were reluctant to purchase a home in anticipation of lower future prices
and more existing home owners found themselves underwater on their mortgage. Coupled with
an increasingly soft labor market and higher unemployment, the number of foreclosures
snowballed despite policy interventions such as mortgage restructuring, first-time homebuyer
tax credits and the Feds purchase of mortgage-backed securities leading to lower interest rates.
One more effect of the high foreclosure rate is a sharp increase in shadow inventory.
Figure 6 illustrates the stock of homes owned by the lender, where quarterly changes equal the
net flows of properties going into foreclosure minus REO inventory that has been resold. During
2004, with prices increasing rapidly, REO inventory was being sold faster than the (small)
number of new foreclosures and shadow inventory fell to its local minimum. However, as house
prices began to fall in 2007, the negative equity position of owners caused foreclosures to
escalate. Initially new foreclosures exceeded REO resales and shadow inventory reached its
peak at the beginning of 2009. In a situation where asset prices are falling dramatically, lenders
were trying to sell properties as quickly as possible. Given the brisk turnover of REO property
along with shadow inventory near historically high levels, there is further reason to believe that
housing prices will continue to decline in a soft Las Vegas housing market.
5. Establishing Causal Relationships
The fan graphs discussed in the previous section are suggestive of intertemporal
relationships between quarterly foreclosures (including deed-in-lieu of foreclosure), median
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price, the quarterly percentage change in median price, and quarterly flipping activity (both buy-
side and sell-side transactions). To establish whether intertemporal relationships actually exist,
we consider Granger causality tests between any two variables of interest. We do not consider
the level of median price explicitly as it is subsumed within the percentage change in median
price variable. While Granger causality methodology is a useful starting point to examine
intertemporal relationships, it is limited in its ability to identify structural relationships between
more than two variables at a time. Thus, with this methodology we simply seek to establish
whether there are unilateral, bilateral, or independent relationships between each variable dyad.
Grangers definition of causality (Granger, 1969) asserts that variable X causes variable
Y if past values of X and Y help explain the variation in current Y better than previous values of
Y itself. Granger Causality is predicated on a rather simple concept. A base-line is established by
using previous values of Y to explain the current value of Y. By adding lagged values of another
variable X, the subsequent model will explain at least as much of the variation in Y as the base-
line model. If the additional explanatory value of the lagged values of X more than outweigh the
lost degrees of freedom associated with the additional explanatory variables, variable X is said to
Granger Cause variable Y. It is possible that variable Y can also Granger cause variable X,
suggesting bilateral feedback between the two variables or a potential third variable that is
causing both X and Y. If neither X nor Y Granger cause each other then the two variables can be
considered independent in the Granger sense of causation even if the correlation between the two
variables is positive.
Before testing the interrelationships between the percentage of transactions that are part
of a flip, the percentage of transactions that are foreclosures, and the percentage change in
median price, it is important to establish the stationarity of each time series. Non-stationarity of
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one or more of the variables could lead to spurious results in any Granger causality test. To
establish stationarity, the first column of Table 4A reports Dickey-Fuller test statistics assuming
no drift for each variable. (Qualitatively similar results were obtained when allowing for drift).
As can be seen in all cases, the null hypothesis of a unit root, i.e., non-stationarity, cannot be
rejected.
It is difficult to reconcile how percentage of transactions that are flips or foreclosures can
actually be non-stationary in the long-run because the two variables are constrained from above
and below. One possibility is that the Dickey-Fuller tests are misleading because of structural
breaks in the data which make the series appear to be non-stationary. The methodology
developed by Zivot and Andrews (1992) tests for non-stationarity after controlling for any data-
determined structural break the procedure discovers. The final two columns of Table 4A report
the results of Zivot-Andrews tests assuming a single structural break in the data. The second
column shows that after controlling for a structural break, the three variables are all stationary.
In other words, the Dickey-Fuller results in column one are likely incorrect.
The final column in Table 4A reports the data-determined structural breaks for the three
variables. The structural breaks are remarkably aligned with anecdotal evidence of when these
characteristics of the Las Vegas housing market experienced fundamental change. For instance,
the percentage change in median price and the percentage of transactions that were flips reveal a
structural break during the first quarter of 2004, exactly when claims of many industry observers
suggested flipping experienced a fundamental increase in popularity. In addition, the percentage
of transactions that were foreclosures reveals a structural break in the second quarter of 2007,
very close to when industry observers suggest the housing market peaked. As median prices
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started to fall, highly leveraged home buyers began to find their mortgages underwater, leading
to a shift in the temporal pattern of foreclosures in the Las Vegas housing market.
The results presented in Table 4A suggest that, after controlling for the structural breaks
in the data, all three variables are stationary. Thus, the standard Granger Causality regression,
where the current value of the dependent variable is regressed on the lagged values of the
dependent and independent variables, is augmented with a dummy variable that takes a value of
zero before the structural break of the independent variable and one thereafter.
The results of the Granger Causality tests are reported in Table 4B. Each dyad between
the three variables involves a pair of Granger Causality tests. The first number in a cell uses only
one-quarter lagged values of both variables, and we label this as short term causation. The
second Granger causality statistic uses four quarters of lagged values of both variables and we
refer to this as longer term causation.
The Granger Causality tests in Table 4B reveal an intriguing set of relationships between
the percentage change in median price, the percentage of transactions that are flips, and the
percentage of transactions that are foreclosures. The first result is that there seems to be
independence between flips and foreclosures (i.e., there is no Granger Causality in either
direction). On the other hand, flips influence percentage change in median price in the longer
term, while foreclosures influence the percentage change in median price in the short term.
Finally, the last row in Table 4B shows that percentage change in median price influences the
other two variables over both one and four quarters.
Thus, while there is some feedback from flips and foreclosures on the percentage change
in price, it is evident that the driving force among these three variables is the percentage change
in price. One explanation for this is that percentage change in price influences the profitability
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(or loss) of flips and foreclosures. As prices increase, regardless of their level, individuals
attempt to reap profits from house flipping. On the other hand, as housing prices fall, more
individuals find their equity eroded and eventually find their mortgages underwater which might
ultimately lead to foreclosure.
A somewhat surprising result from these bivariate tests is the lack of a causal relationship
(in either direction) between flips and foreclosures. Conventional wisdom might suggest that
flips Granger Cause foreclosures, that is, those who bought in the sell-side flip transaction might
have overpaid for the property and be more likely to walk away once underwater. On the other
hand, foreclosures might be expected to influence flips as foreclosures lower the price on the
buy-side of the flip and make flips potentially more profitable. However, there is no causal
relationship between the two suggesting that they are independent of each other in the Las Vegas
market.
6. Policy Discussion and Conclusions
Looking backwards, it is easy to trace through the housing bubble in Las Vegas over the
last decade. The percentage change in price was the driving force behind a surge of flipping
activity that artificially boosted demand for housing in the metropolitan area. This, in turn,
ignited further price increases, and home builders responded by constructing more new homes.
Ultimately growth in the Las Vegas housing stock outstripped population growth and the
resulting moderation in price increases meant that many flips were no longer profitable. As
flipping activity slowed considerably, house prices began to fall. Eventually some homeowners
found their mortgages underwater and defaulted on their notes. These foreclosures led to a
further decline in prices causing more foreclosures in the area.
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That price changes drive foreclosures is consistent with Elul, et al (2010) and Bhutta, et
al (2010) who find that negative equity is a primary reason for default. Together, this work
suggests that loan modification programs will necessarily have limited success in curbing new
foreclosures, and steps must be taken to firm up prices. One possibility is to expand resources
like HUDs Neighborhood Stabilization Program (http://hudnsphelp.info/). This program shores
up demand of foreclosed properties by providing financial assistance to first-time home buyers.
Still another Neighborhood Stabilization Program influences supply of foreclosed homes by
granting funds to government entities for the purpose of demolishing blighted neighborhoods.
Detroit, for example, plans to knock down 3000 homes by September 2010 using federal
government funds. Moreover, Mayor Dave Bing has promised to tear down 10,000 structures in
his first term in office to right-size Detroit and align housing needs with a shrinking city
population (Kellogg, 2010).
Given that excess supply is part of the foreclosure problem, it is perhaps surprising that
new home building continues in Las Vegas. As one Las Vegas builder noted (Streitfeld, 2010),
Were building them because were selling them. Yet new home building continues to add to
the problem of excess supply, falling prices and foreclosed homes. In circumstances like these,
local government units might develop policies that encourage renovation of properties and
rehabilitation of neighborhoods. While declaring a temporary moratorium on new homes may
diminish local tax and permit revenues generated by new housing construction in the short run,
the offset is that city will not have to contend with the costs associated with abandoned homes
and blighted neighborhoods.
At least two other lessons can be derived from the Las Vegas housing market bubble.
First, flipping activity contributed to rising home prices, and given asymmetric information, it
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might be prudent to alert potential homebuyers of legal flipping activity. One way to do that is
to require MLS listings to include information on when the current owner bought the property
and whether the current owner lives in the home. This solution to the asymmetric information
problem is similar to the requirement in several states that sellers divulge information that they
are an agent/owner of a property. (See Levitt and Syverson, 2008, for market distortions related
to the agent/owner problem.)
The second lesson is that municipal governments must be consistent in their record
keeping. Clark County in changing the F transaction code from deed-in-lieu of foreclosure to
foreclosure resale makes it difficult to do any meaningful time comparisons. More importantly,
up to the last three years, foreclosure resales were coded as arms-length transactions (R). But in
the recent downturn, 90% of foreclosure resales were coded F. The upshot is that tax valuation
models and many price indexes will not include these F transactions. In the case of tax valuation
models, excluding foreclosure resales may seriously bias upward the countys assessment of
market value.
Finally, looking forward, it will be important to focus on underlying structural inter-
temporal relationships, perhaps with the use of vector autoregressive models. So, for example,
how did mortgage rates or easy credit influence prices, flipping activity and foreclosures? Or,
did pricing dynamics differ when prices were going up versus down? Answers to questions such
as these will be left for future research.
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Table 1: Temporal Distribution of Residential Transaction Types in Las Vegas, NV
Quarter Total R Total T Total F Pct. T Pct. F Pct. R Quarter Total R Total T Total F Pct. T Pct. F Pct. R
1994q1 3514 102 3 2.82 0.08 97.1 2002q1 7537 364 8 4.6 0.1 95.3
1994q2 4422 77 4 1.71 0.09 98.2 2002q2 8724 447 7 4.87 0.08 95.05
1994q3 4116 79 5 1.88 0.12 98 2002q3 8884 431 16 4.62 0.17 95.21
1994q4 3868 83 4 2.1 0.1 97.8 2002q4 9180 392 6 4.09 0.06 95.85
1995q1 3202 85 1 2.59 0.03 97.38 2003q1 8619 450 8 4.96 0.09 94.95
1995q2 3935 72 7 1.79 0.17 98.04 2003q2 10762 466 10 4.15 0.09 95.76
1995q3 4134 82 3 1.94 0.07 97.99 2003q3 12537 461 10 3.54 0.08 96.38
1995q4 3977 84 4 2.07 0.1 97.83 2003q4 12354 363 7 2.85 0.06 97.09
1996q1 4120 85 2 2.02 0.05 97.93 2004q1 12513 342 6 2.66 0.05 97.29
1996q2 4730 68 5 1.42 0.1 98.48 2004q2 15432 99 14 0.64 0.09 99.27
1996q3 4593 92 2 1.96 0.04 98 2004q3 14635 85 13 0.58 0.09 99.33
1996q4 4755 92 7 1.9 0.14 97.96 2004q4 12907 39 8 0.3 0.06 99.64
1997q1 4064 140 4 3.33 0.1 96.57 2005q1 12360 34 11 0.27 0.09 99.64
1997q2 4867 116 20 2.32 0.4 97.28 2005q2 14970 25 3 0.17 0.02 99.81
1997q3 5049 155 4 2.98 0.08 96.94 2005q3 15453 21 2 0.14 0.01 99.85
1997q4 4923 146 8 2.88 0.16 96.96 2005q4 14302 42 6 0.29 0.04 99.67
1998q1 4469 178 6 3.83 0.13 96.04 2006q1 12414 74 10 0.59 0.08 99.33
1998q2 5669 197 4 3.36 0.07 96.57 2006q2 13267 128 3 0.96 0.02 99.02
1998q3 5645 236 7 4.01 0.12 95.87 2006q3 11741 238 10 1.99 0.08 97.93
1998q4 5727 231 3 3.88 0.05 96.07 2006q4 10511 314 18 2.9 0.17 96.93
1999q1 5406 263 8 4.63 0.14 95.23 2007q1 7801 646 29 7.62 0.34 92.04
1999q2 6656 253 3 3.66 0.04 96.3 2007q2 7451 980 76 11.52 0.89 87.59
1999q3 6453 282 8 4.18 0.12 95.7 2007q3 6103 1432 154 18.62 2 79.38
1999q4 6048 233 6 3.71 0.1 96.19 2007q4 5521 2231 213 28.01 2.67 69.32
2000q1 5654 288 5 4.84 0.08 95.08 2008q1 3515 3060 1607 37.4 19.64 42.96
2000q2 6850 254 9 3.57 0.13 96.3 2008q2 3828 4586 3095 39.85 26.89 33.26
2000q3 6586 319 9 4.61 0.13 95.26 2008q3 4046 5108 4244 38.13 31.68 30.19
2000q4 6756 276 6 3.92 0.09 95.99 2008q4 3755 4473 3861 37 31.94 31.06
2001q1 6600 328 6 4.73 0.09 95.18 2009q1 2787 4319 4127 38.45 36.74 24.81
2001q2 8134 300 5 3.55 0.06 96.39 2009q2 3461 3591 5643 28.29 44.45 27.26
2001q3 7668 309 5 3.87 0.06 96.07 2009q3 4205 4431 4914 32.7 36.27 31.03
2001q4 8086 356 9 4.21 0.11 95.68 2009q4 1842 1787 1639 33.92 31.11 34.97
*Quarterly data from 2009:q4 is not from the entire quarter.
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Table 2: Tax Districts and the Frequency of Transaction Types (Full Sample)
Tax District Number Tax District Name
Total Transactions
R Transactions
T transactions
F transactions
Pct. T & F Transactions
50 Boulder City Library 2,556 2,437 82 37 4.66 107 Laughlin Town Big Bend Colorado River 1,154 1,060 71 23 8.15 125 Artesian Basin Fire 911 1,198 995 114 89 16.94 200 Las Vegas City 164,123 141,063 14,523 8,537 14.05 203 Las Vegas City Redevelopment 1,118 937 125 56 16.19 250 North Las Vegas City 52,169 43,055 5,652 3,462 17.47 254 North Las Vegas Library 31,815 25,911 3,399 2,505 18.56 340 Sunrise Manor 43,009 35,251 4,978 2,780 18.04 410 Winchester Town 2,169 1,885 193 91 13.09 417 Spring Valley Town 46,285 40,262 3,541 2,482 13.01 420 Summerlin Town Artesian Basin 10,468 9,623 478 367 8.07 470 Paradise Town 26,249 23,015 2,110 1,124 12.32 500 Henderson City 3,390 3,027 222 141 10.71 503 Henderson City Redevelopment 1,839 1,556 182 101 15.39 505 Henderson Artesian Basin 49,832 45,255 2,838 1,739 9.18 514 Henderson City Library Debt 4,144 3,878 153 113 6.42 516 Henderson Library Debt/Artesian Basin 18,868 17,137 1,020 711 9.17 521 Henderson City Redevelopment 521 4,522 3,765 514 243 16.74 570 Whitney Artesian Basin 12,419 10,196 1,341 882 17.90 635 Enterprise Fire Artesian Library 911 Manpower 59,379 49,355 5,626 4,398 16.88 901 Mesquite City 4,667 4,430 158 79 5.08
Totals 541,373 464,093 47,320 29,960
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Table 3: Sales around Foreclosures
Quarter
T followed by an R
T followed by an F
F followed by an R
F followed by a T
F or T followed by an R
F or T followed by an F
F or T followed
by a T
Quarterly Average
1994:q1-2006:q4 160.8 0.2 5.0 0.3 165.7 0.2 2.6
2007:q1 174 14 6 1 180 14 11 2007:q2 288 33 4 2 292 33 19 2007:q3 268 86 3 1 271 86 13 2007:q4 370 119 3 2 373 120 16 2008:q1 96 1380 20 5 116 1382 30 2008:q2 85 2772 29 15 114 2779 65 2008:q3 114 3892 85 7 199 3905 43 2008:q4 116 3697 97 10 213 3700 58 2009:q1 126 3896 95 10 221 3902 31 2009:q2 79 5148 160 12 239 5158 53 2009:q3 95 4619 208 20 303 4626 65
2009:q4 76 1571 117 9 193 1571 42
Totals 1994:q1-2009:q4 10246 27237 1086 107 11332 27287 581
*Quarterly data from 2009:q4 is not from the entire quarter.
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Table 4A: Stationarity Tests
Variable Dickey-Fuller Test Zivot-Andrews Test Break Point Percentage Flips -1.097 -5.555* 2004:q1 Percentage Foreclosures 1.274 -4.812* 2007:q2 Pct. Change in Median Price -1.887 -4.635** 2004:q1
P-values indicate rejection of the null hypothesis of a unit root (non-stationarity): * p
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Figure 1: Distribution of Residential Property Transactions by Type and Quarter (2004:q1-2009:q4)
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26
Figure 2: Percent Buy-Side and Sell-Side Flip Transactions (1994:q1-2009:q4)
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Figure 3: Percent Buy-Side and Sell-Side Flip Transactions (2004:q1-2009:q4)
Buy-side flips in 2008 and 2009 do not cover entire two year flipping window.
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Figure 4: Percentage of Transactions that were Flips, Percentage of Transactions Foreclosures, Quarterly Median Price and Percentage Change in Median Price (1994:q1-2009:q4)
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29
Figure 5: Percentage of Transactions that were Flips, Percentage of Transactions Foreclosures, Quarterly Median Price and Percentage Change in Median Price (2004:q1-2009:q4)
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30
Figure 6: Shadow Inventory and Median Prices (2000:q1-2009:q4)
1500
0020
0000
2500
0030
0000
3500
00M
edia
n P
rice
2000
4000
6000
8000
1000
012
000
Sha
dow
Inve
ntor
y
2000q1 2002q3 2005q1 2007q3 2010q1
Shadow Inventory Median Price
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